Are you ready to dive into the realm of image classification using the ConvNeXt Pico OLS model? This power-packed model, trained on the ImageNet-1k dataset, makes it easier than ever to classify images with precision. In this guide, we will walk you through the setup and usage, so you can unleash the full potential of this tool!

Understanding the Model

The ConvNeXt Pico OLS model is fundamentally an image classification backbone, designed to analyze and categorize images effectively. Imagine it as a finely tuned musical instrument, each part working in perfect harmony to create beautiful melodies—here, the melody is the accurate classification of your images. Below are some key stats about the model:

• Parameters: 9.1 million
• GMACs: 1.4
• Activation Complexity: 6.5 million activations
• Image Size: 224 x 224 for training, 288 x 288 for testing

Getting Started with the Model

To start using the ConvNeXt Pico OLS model, follow the steps below:

1. Setting Up Your Environment

You’ll need to have the following libraries installed in your Python environment:

• PIL for image handling
• timm for accessing various models
• torch for tensor calculations

2. Image Classification

Here’s how to classify an image using the ConvNeXt model:

python
from urllib.request import urlopen
from PIL import Image
import timm
import torch

# Load an image
img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))

# Create the model
model = timm.create_model("convnext_pico_ols.d1_in1k", pretrained=True)
model = model.eval()

# Get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

# Classify the image
output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

This code will load an image from a URL, process it, and then pass it through the ConvNeXt model. Finally, it provides you with the top 5 classifications along with their probabilities.

3. Extracting Feature Maps

To extract feature maps from the model, use the following code snippet:

python
# Load the image and model as before
img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))
model = timm.create_model("convnext_pico_ols.d1_in1k", pretrained=True, features_only=True)
model = model.eval()

# Prepare the image
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

# Extract feature maps
output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
for o in output:
    print(o.shape)

Think of this as taking the autopsy of the model after it processes an image—the feature maps tell you the various layers’ response to the inputs.

4. Generating Image Embeddings

Lastly, if you want to get the embeddings of the images, here’s how:

python
# Load the image and model
img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))
model = timm.create_model("convnext_pico_ols.d1_in1k", pretrained=True, num_classes=0)  # remove classifier
model = model.eval()

# Prepare the image
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

# Generate embeddings
output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
output = model.forward_features(transforms(img).unsqueeze(0))  # output is unpooled
output = model.forward_head(output, pre_logits=True)  # output is a (1, num_features) shaped tensor

This code extracts numerical representations of the image, allowing for more advanced analysis and machine learning tasks.

Troubleshooting

• If you encounter an issue with a missing package, ensure you have PIL, torch, and timm installed in your environment.
• Should your image fail to load, verify the URL and ensure it’s accessible.
• If the output is incorrect, double-check your input image size and ensure it’s formatted correctly.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Now you’re equipped with the knowledge to leverage the ConvNeXt Pico OLS model for a variety of image classification tasks. Explore different images and observe how well the model performs! At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.